Hybrid features-enabled dragon deep belief neural network for activity recognition

ABSTRACT

Activity recognition is a challenging task in computer vision that finds widespread applications in various fields, such as motion capture, video retrieval, security, and video surveillance. The objective of this work is to present a technique for recognizing human activities in videos using Dragon Deep Belief Network (DDBN) and hybrid features, which comprises of features like shape, coverage factor, and Space-Time Interest (STI) points. Initially, the keyframes from the input video sequence are extracted using Structural Similarity (SSIM) measure. Then, the features, such as shape, coverage factor, and STI points, are extracted from the keyframes. Based on the feature vector extracted, the proposed DDBN classifier, which is designed by the effective combination of DBN and Dragonfly Algorithm (DA), a classification on human activities, such as walk, bend, etc. in videos. In DDBN, the weights in the network are selected optimally using DA. The weight update using the DA for each incoming feature improves the performance of the DDBN classifier. Further it improves the accuracy in classification of actions. The proposed DDBN classifier is experimented using KTH and Weizmann datasets based on three evaluation parameters, such as accuracy, sensitivity, and specificity. From the performance evaluation, the proposed DDBN classifier could attain better performance with the probability of 98.5% accuracy, 0.96 sensitivity, and 0.959 specificity, respectively.

KEYWORDS:

• Activity recognition
• SSIM
• STI
• DBN
• DA
• DDBN
• keyframe extraction
• optimization
• accuracy

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes on contributors

Paul T. Sheeba has received B.E. degree in Computer Science engineering from Manonmanium Sundaranar University, Trinelveli, India, in 2004, and M.Tech. in Information Technology from Sathyabama University, Chennai, India, in 2007. Currently she is pursuing her Ph.D. in Faculty of Computer Science & Engineering, Sathyabama Institute of Science and Technology. She has held lecturing positions at Kings Engineering College, Chennai, India. She was an Assistant Professor at Loyola Institute of Technology, Chennai, India, for several years. Her research interests cover Artificial Intelligence and Image Processing. She is a Life Member of the Indian Society for Technical Education (ISTE).

S. Murugan received his M.E degree from Sathyabama University with University Rank and Ph.D. from Sathyabama University in the year 2013. He is currently Professor in the Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology. His area of interest includes Web Technology, Cloud Computing, Big Data, Image Processing and Networks. He is also the Software Development Head in Sathyabama Institute of Science and Technology, Member of IEEE (Chennai Chapter) and Computer Society of India (Chennai Chapter).